Identifying gender bias in blockbuster movies through the lens of machine learning

Abstract The problem of gender bias is highly prevalent and well known. In this paper, we have analysed the portrayal roles in English movies, a medium that effectively influences society shaping people’s beliefs opinions. First, gathered scripts films from different genres derived sentiments emotions using natural language processing techniques. Afterwards, converted into embeddings, i.e., way representing text form vectors. With thorough investigation, found specific patterns male female characters’ personality traits movies align with societal stereotypes. Furthermore, used mathematical machine learning techniques some biases wherein men are shown to be more dominant envious than women, whereas women joyful movies. our work, introduce, best knowledge, novel technique convert dialogues an array by combining it Plutchik’s wheel emotions. Our study aims encourage reflections on equality domain film facilitate other researchers analysing automatically instead manual approaches.